With increasing development of technologies, image capture devices have experienced great growth and are rapidly gaining in popularity. For example, an image capture device such as a camera is widely used in the workplace or in the traveling route for capturing an image. Generally, the quality of the image captured by the image capture device is affected by some factors such as the location of the light source or the brightness of the light source. If the location or the brightness of the light source is improper, a back light phenomenon, a side light phenomenon or an insufficient brightness value possibly occurs. In a case of the back light, the light source is behind the subject such that the brightness value at the front side of the subject is very low. In a case of the side light, the light source is located at one side of the subject such that the brightness value at the opposite side of the subject is very low. In a case of the insufficient brightness value, the luminance of the light source is very weak such that the image looks dark.
For overcoming the above drawbacks of uneven brightness values and low brightness values, some methods have been disclosed in order to achieving brightness homogenization or increase the brightness values. These methods include for example a histogram equalization method, a local histogram equalization method, a partially overlapped sub-block histogram equalization method, and a multiple-scale Retinex (MSR) method.
By the histogram equalization method, the brightness values of the image are collected in terms of statistics, the probabilities of respective brightness values are plotted as a cumulative conversion curve, and the converted brightness values are obtained according to the cumulative conversion curve. The distribution of the converted brightness values has an increased contrast. In other words, the histogram equalization method usually increases the contrast of the image. Since the contrast of the image processed by the histogram equalization method is usually over-increased, the processed image looks unnatural.
By the local histogram equalization method, all pixels of the original image are equalized independently and subject to histogram equalization. The contrast increase in the darker region of the image is obvious. The local histogram equalization method, however, needs enormous calculation and also results in unnatural image appearance.
The partially overlapped sub-block histogram equalization method partitions the image into multiple sub-blocks. These sub-blocks are subject to histogram equalization subject to histogram equalization. For preventing over-distortion of the image, these sub-blocks are partially overlapped with each other. The partially overlapped sub-block histogram equalization method can reduce the calculation amount and save calculating time. Since the sub-blocks are processed in replace of pixels, the contrast increase in the darker region of the image is not obvious.
The multiple-scale Retinex (MSR) method is developed and extended from the Retinex image enhancement techniques. The MSR method partitions the image into two parts, i.e. the bright/dark distribution and the image object details. The MSR method blurs the bright/dark distribution and performs a brightness-increasing procedure. After the bright/dark distribution and the image object details are combined together, a more natural image appearance is resulted. In the MSR method, the procedure of blurring the image often partially eliminates acute regions of the image, and thus the acute regions are readily separated apart. In other words, the MSR method results in a useless image with coronas due to the blurring procedure.
Although the above-mentioned techniques can increase the brightness values of the image, these techniques have respective drawbacks. Therefore, there is a need of providing an image processing method of increasing brightness values of the image and avoiding the over-bright or unnatural image appearance.